A Theoretical Development and Analysis of Jumping Gene Genetic Algorithm.

IEEE Transactions on Industrial Informatics (Impact Factor: 3.38). 01/2011; 7:408-418. DOI:10.1109/TII.2011.2158842
Source: DBLP

ABSTRACT Recently, gene transpositions have gained their power andattentionsincomputationalevolutionaryalgorithmdesigns.In 2004, the Jumping Gene Genetic Algorithm (JGGA) was first pro- posedandtwonewgenetranspositionoperations,namely,cut-and- paste and copy-and-paste, were introduced. Although the outper- formance of JGGA has been demonstrated by some detailed statis- tical analyses based on numerical simulations, more rigorous the- oretical justification is still in vain. In this paper, a mathematical model based on schema is derived. It then provides theoretical jus- tifications on why JGGA is superiority in searching, particularly when it is applied to solve multiobjective optimization problems. The studies are also further verified by solving some optimization problems and comparisons are made between different optimiza- tion algorithms.

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    ABSTRACT: Layout planning in a manufacturing company is an important economical consideration. In the past, research examining the facility layout problem (FLP) generally concerned static cases, where the material flows between facilities in the layout have been assumed to be invariant over time. However, in today’s real-world scenario, manufacturing system must operate in a dynamic and market-driven environment in which production rates and product mixes are continuously adapting. The dynamic facility layout problem (DFLP) addresses situations in which the flow among various facilities changes over time. Recently, there is an increasing trend towards implementation of industrial robot as a material handling device among the facilities. Reducing the robot energy usage for transporting materials among the facilities of an optimal layout for completing a product will result in an increased life for the robots and thus enhance the productivity of the manufacturing system. In this paper, we present a hybrid genetic algorithm incorporating jumping genes operations and a modified backward pass pair-wise exchange heuristic to determine its effectiveness in optimizing material handling cost while solving the DFLP. A computational study is performed with several existing heuristic algorithms. The experimental results show that the proposed algorithm is effective in dealing with the DFLP.
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